# Manifest 23维评分算法伪代码
def score_request(request):
    """
    23维度评分，每个维度权重不同
    总分 = Σ(维度分数 × 权重)
    """
    dimensions = {
        'complexity': analyze_complexity(request),           # 复杂度
        'length': analyze_length(request),                 # 长度
        'has_code': detect_code(request),                  # 是否含代码
        'has_math': detect_math(request),                # 是否含数学
        'has_reasoning': detect_reasoning(request),       # 是否需推理
        'language': detect_language(request),             # 语言
        'domain': detect_domain(request),                 # 领域
        # ... 共23个维度
    }
    
    tier = select_tier(total_score)
    # tier: simple / standard / complex / reasoning
    
    model = select_best_model_in_tier(tier)
    return model

# 一键安装
openclaw plugins install manifest

# 重启网关
openclaw gateway restart

# 访问仪表板
# http://127.0.0.1:2099

# 拉取镜像
docker pull manifestdotbuild/manifest

# 运行容器
docker run -d \
  --name manifest \
  -p 2099:2099 \
  -v manifest_data:/app/data \
  manifestdotbuild/manifest

# 访问仪表板
open http://127.0.0.1:2099

# 访问 https://app.manifest.build
# 按指引配置 API Keys

# 配置 Provider API Keys
# 在仪表板 http://127.0.0.1:2099 中配置

# 或通过环境变量
export OPENAI_API_KEY=sk-...
export ANTHROPIC_API_KEY=sk-ant-...
export GOOGLE_API_KEY=AIza...

# 无需修改代码！Manifest 自动拦截 manifest/auto 请求

# 简单问题 → 自动路由到便宜模型
response = openai.ChatCompletion.create(
    model='manifest/auto',  # 神奇的地方！
    messages=[{'role': 'user', 'content': '今天天气如何？'}]
)

# 复杂问题 → 自动路由到强大模型
response = openai.ChatCompletion.create(
    model='manifest/auto',
    messages=[{'role': 'user', 'content': '用Python写一个快速排序'}]
)

# 强制使用某个 tier
response = openai.ChatCompletion.create(
    model='manifest/simple',      # 最便宜的模型
    messages=[...]
)

response = openai.ChatCompletion.create(
    model='manifest/reasoning',  # 推理专用模型
    messages=[...]
)

# 直接指定某个模型（绕过路由）
response = openai.ChatCompletion.create(
    model='manifest/gpt-4.1',
    messages=[...]
)

# Manifest 自动分析请求并路由到最佳模型
def smart_routing(request):
    """
    请求流程:
    1. 分析请求特征 (23维度)
    2. 计算总分
    3. 选择 Tier
    4. 在该 Tier 中选择最佳模型
    """
    score = analyzer.score(request)
    tier = selector.select_tier(score)
    model = router.select_model(tier, request)
    
    return model

# 当一个模型失败时，自动切换到下一个
@retry_on_failure(max_retries=3)
def call_with_fallback(request):
    providers = ['gpt-4.1', 'claude-sonnet-4.5', 'gemini-2.5-pro']
    
    for provider in providers:
        try:
            return call_model(provider, request)
        except Exception as e:
            logger.warning(f"{provider} failed: {e}")
            continue
    
    raise AllProvidersFailedError()

# 设置每月预算
manifest.set_budget(monthly_dollars=100)

# 设置每个模型的 Rate Limit
manifest.set_rate_limit('gpt-4.1', requests_per_minute=60)

# 查看消费
manifest.get_usage_stats()

# 添加自定义 Provider
manifest.add_provider(
    name='my-custom-provider',
    api_base='https://api.my-provider.com/v1',
    api_key='sk-...',
    models=['my-model-1', 'my-model-2']
)

# Manifest 优势
1. 免费 (无 5% 手续费)
2. 完全本地部署，数据不外泄
3. 开源透明，可审计
4. 支持本地模型 (Ollama)

import openai

# 使用 manifest/auto 自动路由
client = openai.OpenAI(
    api_key='dummy',  # Manifest 不需要真实 key
    base_url='http://127.0.0.1:2099/v1'
)

response = client.chat.completions.create(
    model='manifest/auto',
    messages=[{'role': 'user', 'content': 'Hello!'}]
)

from manifest import Manifest

m = Manifest()

# 自动路由
result = m.generate('What is 2+2?')

# 指定 tier
result = m.generate('Write a Python quicksort', tier='complex')

# 指定模型
result = m.generate('Write a quicksort', model='manifest/gpt-4.1')

# 获取使用统计
stats = m.get_stats()

print(f"Total cost: ${stats['cost']}")
print(f"Total tokens: {stats['tokens']}")
print(f"Savings vs GPT-4: {stats['savings']}%")

# 按模型查看
for model, data in stats['by_model'].items():
    print(f"{model}: ${data['cost']}, {data['tokens']} tokens")

// manifest.config.json
{
  "tiers": {
    "simple": {
      "max_cost_per_1k": 0.001,
      "models": ["gpt-4o-mini", "haiku-4.5"]
    },
    "standard": {
      "max_cost_per_1k": 0.01,
      "models": ["gpt-4o", "sonnet-4.5"]
    },
    "complex": {
      "max_cost_per_1k": 0.1,
      "models": ["gpt-4.1", "opus-4.6"]
    },
    "reasoning": {
      "max_cost_per_1k": 1.0,
      "models": ["o3", "o4-mini"]
    }
  }
}

# 强制某些请求使用特定模型
manifest.add_rule(
    condition=lambda req: 'code' in req.content.lower(),
    model='manifest/gpt-4.1'
)

manifest.add_rule(
    condition=lambda req: '数学' in req.content or '证明' in req.content,
    model='manifest/reasoning'
)

# 配置备份链
manifest.set_fallback_chain([
    'manifest/gpt-4.1',      # 首先尝试
    'manifest/claude-sonnet-4.5',  # 备份 1
    'manifest/gemini-2.5-pro',    # 备份 2
])

# ✅ 推荐：让 Manifest 自动选择
model = 'manifest/auto'

# ✅ 推荐：简单任务使用 simple tier
model = 'manifest/simple'  # 便宜 10x

# ❌ 不推荐：强制使用最贵模型
model = 'manifest/opus-4.6'  # 除非必要

# ✅ 推荐：批量请求
batch = [f"Item {i}" for i in range(100)]
results = m.generate_batch(batch)

# ✅ 推荐：使用流式输出
for token in m.generate_stream('Write a story'):
    print(token, end='', flush=True)

# 启用调试模式
m.set_debug(True)

# 查看路由决策
decision = m.trace('What is 2+2?')
print(f"Tier: {decision.tier}")
print(f"Model: {decision.model}")
print(f"Reason: {decision.reason}")